While BLE is a short-range and low-power technology mainly aiming for Internet of Things (IoT) development [1]}, the accessibility of BLE signals, even in those less-visited areas, has made BLE an alternative choice for RSS fingerprinting.
Many fingerprint-based localization techniques leverage the broadcasting feature of BLE beacons to estimate the location of a user holding a smartphone [2]}, [3]}, [4]}.
The work in [2]} exploits the RSS values measured from all the deployed beacons to construct a location fingerprint.
The work in [3]} uses a deep learning approach to learn a robust fingerprint representation for 3D localization.
On the other hand, [4]} computes the proximity information based on the Gaussian process regression to provide loosely location information.
Overall, none of these works consider the fingerprint variability due to the dynamic holding gesture by different users.
